Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study

JMIR MEDICAL INFORMATICS Guzzi et al

Original Paper Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study

Pietro H Guzzi1*, PhD; Giuseppe Tradigo2*, PhD; Pierangelo Veltri1, PhD 1Department of Surgical and Medical Sciences, University of Catanzaro, CZ, Italy 2Ecampus University, Novedrate, Italy *these authors contributed equally

Corresponding Author: Pietro H Guzzi, PhD Department of Surgical and Medical Sciences University of Catanzaro Catanzaro CZ, Italy Phone: 39 09613694148 Email: [email protected]

Abstract

Background: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. Objective: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. Methods: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. Results: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. Conclusions: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations.

(JMIR Med Inform 2021;9(3):e18933) doi: 10.2196/18933

KEYWORDS COVID-19; data analysis; ICU; management; intensive care unit; pandemic; outbreak; infectious disease; resource; planning

virus caused a high number of infections. Only a fraction of Introduction patients who are infected need hospitalization in ICUs, a COVID-19 is a disease that was reported in Wuhan, China in relatively high number of which do not survive the virus. In December 2019 [1]. It has been stressing health structures and Italy, in almost 2 months during the first peak, there have been governments worldwide due to the difficulties in containing its more than 100,000 known infections and nearly 35,000 diffusion [2-4]. The virus appears as a flu, but it attacks the COVID-19±related deaths [5-7]. pulmonary apparatus, and on average, 6% of symptomatic The virus also spread in other countries across the world with patients require hospitalization in intensive care units (ICUs) different modalities and aggressiveness. During its first wave, due to severe respiratory syndromes. The rapid diffusion of the from March to May 2020, Italy had the second highest number

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 1 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

of patients who were infected. All countries reported difficulties our model as being scalable at both the regional and subregional in answering to the high number of requests for ICU beds and levels. We claim that it can be used in different countries and reliable detection of the real infection numbers. In countries in future contexts where virus diffusion will require where the virus spread later with respect to Italy, such as the well-planned health resource management [13,25]. United States, France, Spain, and other European countries, it The paper is structured as follows. The Methods section reports diffused with similar trends but with different absolute numbers, the proposed assessed model and the Italian infection data. The as reported by the World Health Organization [3,8-13]. Results section reports the application of the model on three Nevertheless, the impact on health structure resources has been sample regions out of 20, Lombardia (north), Toscana (central), similar. In fact, the number of infections detected is strictly and Sicilia (south). The Discussion section reports on the related to the number of swab tests performed in the population. limitations of this study and comparisons with other work. Tradigo et al [14] assessed the real number of people who are However, our model is general enough to be successfully applied infected with respect to the known ones. In contrast, the number to other pandemic situations in other contexts. of infections is much higher than the known ones and includes patients who are asymptomatic (ie, those who got the infection but who did not manifest any symptoms). The number of ICU Methods beds is always related to the number of real infections. ICU Situation in Italy Regions such as Lombardia in the north of Italy have been Italy was affected by COVID-19 by the end of January 2020, strongly affected by COVID-19, and it seems that this is due to starting from northern regions such as Lombardia and Veneto. a large number of patients who were asymptomatic already in By the end of February, the increasing trend of infection the middle of January 2020 and late adoption of containment numbers per day obliged the governments at the regional measures such as limitation of circulation and delay in applying levelÐand at the national level starting on March 10±to rules such as smart working [15-17]. introduce containment measures. We focus on the problem of rapidly estimating resources during For example, on March 26, 2020, in Italy, we had 24,747 total the exponential phase of the COVID-19 emergency, in reported COVID-19 infection cases, of which 20,603 had the particular, being aware of the differences among regions in disease, 1809 had died, and 2335 had recovered from it. terms of health structure resources such as ICU beds. We show Regarding patients who were infected, 9268 were treated in how the proposed model scales at the regional level and how it their homes since they did not have severe illness, 9663 were can help decision makers plan expansions of resources near hospitalized, and 1672 were admitted to ICUs. The trend saturation or reroute patients to neighboring regions. continued increasing until April 19, 2020, which has been the The prediction of COVID-19 diffusion is a relevant problem, peak of COVID-19 infections in Italy. and it has been discussed in other papers [18-20]. Some papers In reaction to the exponential growth of patients who are do not consider the regional level and local differences that are infected that require hospitalization, one possible measure relevant in some countries such as Italy. For instance, Li et al adopted by many countries has been to build emergency [21] developed a model starting from Hubei Province data, and hospitals dedicated to patients with COVID. In Italy, one they used the model for prediction in other countries such as strategy consisted in improving existing structures by extending Italy and Korea. Other papers did not consider the prediction the number of ICU resources and beds, and using dedicated of ICU resources. Conversely, our study is scalable on a regional health structures. For example, one study [26] focuses on level, and it is able to predict ICU needs. In a previous study accelerating the process of acquiring and furnishing hospitals [22-24], we developed a preliminary model for resource with assisted breathing devices. planning; here, we present an extension of the model with a particular focus on the assessment of the predictions. Italy has approximately 5200 ICU beds in total, which have been dimensioned by design to be equal to 80% of their average The model presented here has been assessed comparing the occupancy at any given time. In addition, they are allocated at simulated and predicted resource values with the measured ones. a regional level proportional to the local population and are The rapid diffusion trend in high-income countries'populations usually managed locally. Table 1 reports the ICU bed and in cities with high density stressed the health structure in distribution among regions associated with the demography. many countries. Indeed, a small portion of patients with The COVID-19 pandemic called these choices into question, COVID-19 require ICU admission. The exponential diffusion thus introducing the necessity of emergency units in cities where in terms of an increased number of infections per day required the virus rapidly diffused and where existing resources were a larger number of ICU beds than the ones available. We report limited.

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 2 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

Table 1. Distribution of ICU beds in each Italian region ordered by regional population. The number of beds could increase in the future due to government investments for the emergency.

Region ICUa beds, n Population, n ICU beds per citizen (%) Lombardia 1067 10,060,574 0.0106 Lazio 590 5,879,082 0.0100 Campania 350 5,801,692 0.0060 Sicilia 346 4,999,891 0.0069 Veneto 498 4,905,854 0.0102 Emilia Romagna 539 4,459,477 0.0121 Piemonte 320 4,356,406 0.0073 Puglia 210 4,029,053 0.0052 Toscana 450 3,729,641 0.0121 Calabria 110 1,947,131 0.0056 Sardegna 150 1,639,591 0.0091 Liguria 70 1,550,640 0.0045 Marche 108 1,525,271 0.0071 Abruzzo 73 1,311,580 0.0056 Friuli Venezia Giulia 80 1,215,220 0.0066 Trentino Alto Adige 71 1,072,276 0.0066 Umbria 30 882,015 0.0034 Basilicata 49 562,869 0.0087 Molise 30 305,617 0.0098 Valle D'Aosta 15 125,666 0.0119 Italy (total) 5156 60,359,546 0.0085

aICU: intensive care unit. Because of ICU bed limitations, many patients have been moved grouping of patients with COVID-19 in dedicated health from ICUs to subintensive units or to other regions to free up structures. It is trivial that such a decision must be based on the spaces. Indeed, ICU slots are often used for treating postsurgery correct estimation of ICU beds that are occupied by patients, patients and patients affected by pulmonary diseases. At the but this estimation is still a matter of discussion [26]. date of the peak (ie, April 19, 2020), almost 2635 ICU beds were occupied, 108,257 infections were confirmed by swab Model Description and Assessment tests, and 25,033 patients had recovered without using ICU We report here the description and assessment of the proposed beds. Thus, most of the infections were asymptomatic, and model by using Italian cases. patients quarantined at home. Even if the number of required We start by considering a time window of six consecutive ICU beds is less than the total number of available ICU beds in infection values (one reading per day) from the official Italian Italy (see Table 1), the infection distribution is not homogeneous COVID-19 data set. We then calculate an exponential fitting among regional departments and does not follow a regular function for these values, since we know that the viral phase geographical distribution. follows an exponential growth. Thus, performing a flexible and reliable model that can predict In Figure 1, the first four time windows (ie, 6, 7, 8, and 9 days) and control resource requirements and distribution at a regional and the related fitting functions are reported. The exponential scale is required. The number of patients in the ICUs is also related to the requests of other clinical units such as emergency fitting function for the first window is , where x is units for non±COVID-19, but still serious, diseases (eg, time (ie, days) and y is the number of infections. We use the cardiovascular-affected patients). calculated fitting equation to predict the number of patients infected with COVID-19 for the succeeding days; for the first Moreover, considering that the average survival time for patients time window, we predicted 1086 total infections on the seventh with COVID-19 that die has been measured to be approximately day. We compared the predicted value with the observed 10 days after ICU admission, the need to plan resources is infections (ie, real number of infections) from the data set, and urgent. It may involve making new ICU beds and planning we calculated the difference and the percentage increase, which logistics to move patients among regions or to optimize the will be useful during the assessment phase. We then proceeded

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 3 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

by extending the time window by 1 day, and we redid all the In the second step, we consider the number of occupied ICU steps (exponential fitting, prediction of the infections for the beds as a function of the number of COVID-19 infections. In succeeding day, difference, and percentage increase). this case, we adopted the weighted average as a fitting function. Figure 3 depicts this correlation (in blue) between total To assess the precision of the first step, we considered the infections (x-axis) and ICU beds occupied (y-axis) together calculated percentage increase between the predicted values with the weighted average fitting for the whole data set (in light and the observed ones. As reported in Figure 2, the percentage blue). increase was around 40%.

Figure 1. Exponential fitting of infection levels for the first four time windows. Each is longer than the previous by 1 day. The shown time windows are W1 (6 days), W2 (7 days), W3 (8 days), and W4 (9 days).

Figure 2. Percentage increase between the predicted and the observed number of infections. On the x-axis, we have time windows and, on the y-axis, the percentage increase of the predicted infection values with respect to the observed ones.

Figure 3. Correlation between occupied ICU beds and COVID-19 infections. ICU: intensive care unit.

For each time window (W1, W2, ¼, W15; see Figure 4), we predicted and the observed ICU values, and similarly to the first consider three adjacent data point coordinates (infections for step, we reported the percentage increase between the two. Table the x-axes and ICU for the y-axes) to calculate a linear equation 2 reports an example of percentage increase values of the as a weighted average fitting function for the values contained predicted versus observed ICU resources for an Italian region's in it. We then used such a function to estimate the future ICU data set. The percentage increase is above 40% for only a few bed occupation for the following day by using the predicted values, but the majority are near 20%, as represented in Figure infected value. We then calculated the difference between the 5.

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 4 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

Figure 4. Time windows W1, W2, ¼, W15 are defined incrementally starting from a period of 6 consecutive days, which has been considered the minimum number of data points to calculate the fitting function.

Table 2. Observed versus predicted ICU beds for the Lombardia Region for each time window (W1, ..., W15) considered in the time period from February 24 to March 15, 2020.

Observed ICUa bedsb, n Predicted ICU bedsc, n Percentage increase (%) 106 172 62 127 146 15 167 201 20 209 262 25 244 371 52 309 380 23 359 552 54 399 491 23 440 537 22 466 521 12 560 892 59 605 700 16 650 841 29 732 893 22 767 930 21

aICU: intensive care unit. bObserved (ie, real) ICU beds measured during the COVID-19 emergency. cCalculated by our model.

Figure 5. Percentage increase between the predicted and the observed ICU beds occupied. ICU: intensive care unit.

We applied the described method both at the national and discuss the limitations of the current model and its application regional scale, and we report the results in the next section. to the Italian use case at a regional level. We show that, by using our method, it has been possible to predict future ICU bed Results occupancy with fair accuracy.

In this section, we show the application of the described model The proposed model works in the exponential phase of the to the Italian official COVID-19 data set [27], and we briefly infection spread, while for the nonexponential stage, other

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 5 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

models can be used such as the Verhulst logistic model [9]. In one reading per day for each region. We selected the features its present form, the model is tailored to the Italian COVID-19 of interest and aggregated the tuples by region before estimating data set. However, with minimal adaptation, it could work with the prediction model parameters. For each region, we considered other data sets of infectious diseases with different data schemas. the number of infections, and we calculated an exponential fitting equation. By using such an equation, we were able to The presented model works in the exponential phase of the estimate the number of patients who will be infected in the infection. Model sensitivity has not been considered in the case succeeding days. We then considered the relation between ICUs of this model because we focused only on the exponential and infections, with which we can use the predicted infection growth of the infections, which is the crucial moment that ICU levels to estimate future values of ICU and resource occupation. bed and resource availability are most stressed and inadequate. These predictions can be a valuable tool for rapidly planning Changing the assumption (ie, nonexponential growth) is ICU resources in case of shortages during clinical emergencies considering a time window in which ICU resources are surely in general, for instance, by reallocating patients in other regions available with respect to the requests. For instance, we with lower levels of ICU occupancies. performed experiments that modeled the nonexponential In Figure 6 (upper left), we report COVID-19 data about the infection phase with a Verhulst logistic model, but again, when Lombardia region between February 24 and March 15, 2020, a there is no emergency, the ICU predictions are not useful since time period in which infection levels (in blue) were growing in resources are largely available. an exponential fashion. We report the exponential fitting The final goal is to predict the number of future beds needed in function for the infections (in light blue), the number of the ICUs as a function of the level of infection in a given region. hospitalized with symptoms (in red), and the number of deaths The ability to predict future resource occupation can be a related to COVID-19 (in yellow). Figure 6 (upper right) depicts powerful and useful tool for local decision makers with the the occupied ICU beds as a function of the number of people responsibility of managing and optimizing clinical resources infected with COVID-19 in the same aforementioned time during the emergency. period. The fitting function (in light blue) is a weighted average with a modulus of 3, with which we predict future ICU bed We report the application of the presented model for three Italian occupation from a given infection value. regions: Lombardia, Toscana, and Sicilia, which represent a balanced sample of northern, central, and southern Italian Similar to the Lombardia region, we report the application of regions. We extracted and transformed the relevant data from the proposed model to COVID-19 infection data and resource the official Italian COVID-19 data set and considered the total necessity prediction for both the Toscana and Sicilia regions number of patients who were infected and the ICU beds (central and lower part of Figure 6, respectively). We report occupied by patients with COVID-19 reported by the various three example regions (Figure 6) to represent the northern local health structures. regions (ie, Lombardia; which are the more affected part of the country), the central regions (ie, Toscana), and the south (ie, In the data set, we have 17 total features (eg, latitude, longitude, Sicilia). They are needed to show how COVID-19 diffused date, the total number of infections, number of patients who are differently from the north to the south of Italy. hospitalized, number of deaths, and number of recoveries) and

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 6 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

Figure 6. The figure depicts Lombardia, Toscana, and Sicilia regions as representative of infection situations in the northern, central, and southern regions of Italy, respectively, in a time window between days (0 days to 21 days; ie, February 24 to March 15, 2020) of infection in the official data set (ie, 3 weeks). In the left column, we report the number of infections per day, while in the right column, we have the occupied ICU beds as a function of the number of COVID-19 infections. In light blue, we report the fitting functions for the considered data (left column: exponential function; right column: weighted average with a modulus of 3). The red lines in the left column show the hospitalized patients with symptoms, and the yellow lines show the number of deaths. ICU: intensive care unit.

Discussion Comparison With Prior Work Modeling of the COVID-19 spread is currently a hot topic Limitations considering the pandemic. Consequently, many different works The presented model only works in a scenario of an exponential have been proposed. Some of them are based on a deterministic growth of infections, since it is a crucial moment in which ICU model that uses ordinary differential equations for predicting bed and resource availability are most stressed and inadequate. the number of infected people (eg, [11,12,28]). Some other approaches use Markov modeling and compartmental models The nonexponential phase might be modeled, for instance, by (eg, [29-31]). To the best of our knowledge, only the work of using models such as the Verhulst logistic one. However, our Rossman et al [10] presents a scalable granularity (at a state focus is on time windows in which resources are scarcely level). With respect to those works, we also tried to predict ICU available with respect to the rapidly increasing requests, such needs (including data about existing ICU occupancy and the as ICU beds in the COVID-19 pandemic. trend of ICU use) with the aim to support health care managers. Another limitation of the proposed model regards the Conclusion impossibility of considering predictions too far ahead in the The COVID-19 pandemic has been characterized by the rapid future, limiting the applicability of the prediction to a few days. spread of an aggressive virus, which has stressed the health However, this is generally sufficient to help in planning ICU system. We think that patient management is strictly related to resources during an emergency. the ability of health structures to deal with this kind of disease, which requires nonstandard protocols such as the use of respiratory devices. We think that, by using a scalable predictive

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 7 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

model at regional and district levels, the granularities may the virus will cyclically reappear in the near future. To this end, support decision makers (eg, national governments) in better the proposed model could be applied during these new outbreaks managing the emergency. and as a decision support tool in other similar pandemics or situations where resource prediction is necessary. The COVID-19 pandemic has reached different regions in various countries worldwide. Furthermore, it is expected that

Acknowledgments We thank Italian Protezione Civile for freely providing online data, which allows studies on the COVID-19 pandemic.

Conflicts of Interest None declared.

References 1. Malta M, Rimoin AW, Strathdee SA. The coronavirus 2019-nCoV epidemic: is hindsight 20/20? EClinicalMedicine 2020 Mar;20:100289 [FREE Full text] [doi: 10.1016/j.eclinm.2020.100289] [Medline: 32154505] 2. Arabi YM, Fowler R, Hayden FG. Critical care management of adults with community-acquired severe respiratory viral infection. Intensive Care Med 2020 Feb;46(2):315-328 [FREE Full text] [doi: 10.1007/s00134-020-05943-5] [Medline: 32040667] 3. Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 2020 Apr 24;368(6489):395-400 [FREE Full text] [doi: 10.1126/science.aba9757] [Medline: 32144116] 4. Day M. Covid-19: Italy confirms 11 deaths as cases spread from north. BMJ 2020 Feb 26;368:m757. [doi: 10.1136/bmj.m757] [Medline: 32102793] 5. Gaeta G. Data analysis for the COVID-19 early dynamics in Northern Italy. arXiv. Preprint posted online March 4, 2020. 6. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020 Apr 30;382(18):1708-1720. [doi: 10.1056/nejmoa2002032] 7. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020 Feb 15;395(10223):497-506 [FREE Full text] [doi: 10.1016/S0140-6736(20)30183-5] [Medline: 31986264] 8. WHO Coronavirus Disease (COVID-19) Dashboard. World Health Organization. 2020. URL: https://covid19.who.int/ [accessed 2020-07-05] 9. Bacaër N. Verhulst and the logistic equation (1838). In: A Short History of Mathematical Population Dynamics. London: Springer; 2011:35-39. 10. Rossman H, Keshet A, Shilo S, Gavrieli A, Bauman T, Cohen O, et al. A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys. Nat Med 2020 May;26(5):634-638 [FREE Full text] [doi: 10.1038/s41591-020-0857-9] [Medline: 32273611] 11. Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med 2020 Mar 13;27(2) [FREE Full text] [doi: 10.1093/jtm/taaa021] [Medline: 32052846] 12. Tang B, Bragazzi NL, Li Q, Tang S, Xiao Y, Wu J. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). Infect Dis Model 2020;5:248-255 [FREE Full text] [doi: 10.1016/j.idm.2020.02.001] [Medline: 32099934] 13. Zhao S, Musa SS, Lin Q, Ran J, Yang G, Wang W, et al. Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak. J Clin Med 2020 Feb 01;9(2) [FREE Full text] [doi: 10.3390/jcm9020388] [Medline: 32024089] 14. Tradigo G, Guzzi PH, Veltri P. On the assessment of more reliable COVID-19 infected number: the italian case. medRxiv. Preprint posted online March 27, 2020 2021. [doi: 10.1101/2020.03.25.20043562] 15. Keeling MJ, Rohani P. Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press; 2008. 16. Liew MF, Siow WT, MacLaren G, See KC. Preparing for COVID-19: early experience from an intensive care unit in Singapore. Crit Care 2020 Mar 09;24(1):83 [FREE Full text] [doi: 10.1186/s13054-020-2814-x] [Medline: 32151274] 17. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020 Feb 22;395(10224):565-574 [FREE Full text] [doi: 10.1016/S0140-6736(20)30251-8] [Medline: 32007145] 18. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ 2020 Apr 07;369:m1328 [FREE Full text] [doi: 10.1136/bmj.m1328] [Medline: 32265220]

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 8 (page number not for citation purposes) XSL·FO RenderX JMIR MEDICAL INFORMATICS Guzzi et al

19. Huang C, Xu X, Cai Y, Ge Q, Zeng G, Li X, et al. Mining the characteristics of COVID-19 patients in China: analysis of social media posts. J Med Internet Res 2020 May 17;22(5):e19087 [FREE Full text] [doi: 10.2196/19087] [Medline: 32401210] 20. Ambikapathy B, Krishnamurthy K. Mathematical modelling to assess the impact of lockdown on COVID-19 transmission in India: model development and validation. JMIR Public Health Surveill 2020 May 07;6(2):e19368 [FREE Full text] [doi: 10.2196/19368] [Medline: 32365045] 21. Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, et al. Propagation analysis and prediction of the COVID-19. Infect Dis Model 2020;5:282-292 [FREE Full text] [doi: 10.1016/j.idm.2020.03.002] [Medline: 32292868] 22. Rezaeetalab F, Mozdourian M, Amini M, Javidarabshahi Z, Akbari F. COVID-19: a new virus as a potential rapidly spreading in the worldwide. J Cardio-Thoracic Med 2020;8(1):563-564. 23. Yaesoubi R, Cohen T. Generalized Markov models of infectious disease spread: a novel framework for developing dynamic health policies. Eur J Oper Res 2011 Dec 16;215(3):679-687 [FREE Full text] [doi: 10.1016/j.ejor.2011.07.016] [Medline: 21966083] 24. Guzzi PH, Tradigo G, Veltri P. Spatio-temporal resource mapping for intensive care units at regional level for COVID-19 emergency in Italy. Int J Environ Res Public Health 2020 May 12;17(10) [FREE Full text] [doi: 10.3390/ijerph17103344] [Medline: 32408508] 25. Jung S, Akhmetzhanov AR, Hayashi K, Linton NM, Yang Y, Yuan B, et al. Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. J Clin Med 2020 Feb 14;9(2) [FREE Full text] [doi: 10.3390/jcm9020523] [Medline: 32075152] 26. Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet 2020 Apr 11;395(10231):1225-1228 [FREE Full text] [doi: 10.1016/S0140-6736(20)30627-9] [Medline: 32178769] 27. pcm-dpc / COVID-19. GitHub. URL: https://github.com/pcm-dpc/COVID-19 [accessed 2020-07-05] 28. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 2020 Feb 29;395(10225):689-697 [FREE Full text] [doi: 10.1016/S0140-6736(20)30260-9] [Medline: 32014114] 29. Arango-Londoño D, Ortega-Lenis D, Muñoz E, Cuartas D, Caicedo D, Mena J, et al. Predicciones de un modelo SEIR para casos de COVID-19 en Cali, Colombia. Rev salud pública 2020 Mar 01;22(2):1-6. [doi: 10.15446/rsap.v22n2.86432] 30. Dye C, Gay N. Modeling the SARS epidemic. Science 2003 Jun 20;300(5627):1884-1885. [doi: 10.1126/science.1086925] [Medline: 12766208] 31. Gatto M, Bertuzzo E, Mari L, Miccoli S, Carraro L, Casagrandi R, et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc Natl Acad Sci U S A 2020 May 12;117(19):10484-10491 [FREE Full text] [doi: 10.1073/pnas.2004978117] [Medline: 32327608]

Abbreviations ICU: intensive care unit

Edited by G Eysenbach; submitted 27.03.20; peer-reviewed by B Smith, M Hamasha; comments to author 04.05.20; revised version received 05.06.20; accepted 17.01.21; published 09.03.21 Please cite as: Guzzi PH, Tradigo G, Veltri P Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study JMIR Med Inform 2021;9(3):e18933 URL: https://medinform.jmir.org/2021/3/e18933 doi: 10.2196/18933 PMID: 33629957

©Pietro H Guzzi, Giuseppe Tradigo, Pierangelo Veltri. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.03.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

https://medinform.jmir.org/2021/3/e18933 JMIR Med Inform 2021 | vol. 9 | iss. 3 | e18933 | p. 9 (page number not for citation purposes) XSL·FO RenderX